AI in Fintech

AI in Fintech industry

In the last few years, AI in Fintech has seen some significant advancements. As a result, AI is rapidly altering the way business is conducted, they have become like cogs working together. In fact, around 56% of firms use AI in at least one business function, according to McKinsey research.  

FinTech industries are taking the maximum advantage of AI’s advanced insights about consumer behavior and they seem to grow at a rapid pace. With AI technology, fintech companies keep in touch with their customers all the time, creating an environment of well-being and making them feel safe, through automating customer support, the improvement fraud detection and simplifying decision-making with data and predictive analysis. 

Advantages of using AI in fintech  

It is not a secret that AI in fintech solutions can generate great value in our lives, let see all the advantages of using it: 

– Increased security driven by authentication: applying this, makes it more difficult for hackers to exploit because added an extra effort to compromise digital devices and networks through unauthorized access. 

– Behavior-based investment predictions: machine learning has become an important part when it comes to analyzing behavior. Using artificial intelligence in fintech would offer insightful forecasts about exchange rates, investments, and short- or long-term trends, all this became possible through modifying marketing strategies and observing consumer behavior. 

– Automated data processing: for all the data processors this automation has been a relief because right now they can use the time they use in processing to analyze reports. Thanks to AI-based software that enhances data processing or takes over administrative duties like invoicing, we can see powerful systems can achieve a balance between security and agility. AI can also conduct in-depth customer data analyses and make predictions about consumer preferences, product development, and distribution methods. 

 – Human-like interaction with customers: most known as chatbots, AI-based trained data models for natural language processing (NLP) and natural language understanding (NLG) to comprehend human communication using text language and correspondingly communicating, these will not only personalized but also speed communication making it simpler and more convenient for clients. We know that more happy clients and customer service staff is translate into a more successful business. 

– Cost-saving:  44% of businesses adopt AI technology to lower company costs in areas, according to McKinsey research. With AI automation we not only save time but also, we save reduce operational costs. Thought assessment algorithms and predictive analytics we can guide customers to choose the right product or service helping the financial industry to improve their products and user experience. 

Use cases and examples of AI in fintech 

So far we have seen the advantages of artificial intelligence in the financial industry, but in what cases is it currently being used? Here are some examples: 

– Automating financial reporting: it is not a secret that banks and financial industries have a massive among of data, and they use it to create reports that help the company improve their services, consequently, they need such reports repeatedly, even though the input data sets could be different. AI process all this data and creates reports faster than a human being, this way they reduce time and cost.  

Customer experience: Through personalization, your brand loyalty and customer confidence are improved. For example, when clients download banking apps, customer data is gathered and analyzed by AI systems, then this information is used to provide pertinent, pre-approved products and specialized financial advice. In banking apps, AI can help users track their spending goals and financial objectives. 

– Detecting banking fraud: Artificial intelligence can respond faster to the data supplied to them through users’ behavior patterns, by being monitored with the recognition of patterns and correlations. In this way, they can identify what kind of actions deviate from the normal and could show fraud attempts or incidents and maybe even spot fraudulent activities. This relieves the work they used to have and frees them to concentrate on higher-level problems. 

– Data analytics: AI algorithms analyze a very large number of data points, and they can execute trades at an optimal price. Several smartphone apps with AI backing now examine historical and current data about businesses and their stocks. This mitigates risks more efficiently and offers you what stocks are the bets for investment and which would be a bad choice. 

AI in fintech apps 

Thanks to artificial intelligence and machine learning, the efficiency and accuracy of analytics have been increased, making customer interaction faster and more value-adding. The best thing is that they help industries conduct a more personalized approach toward their target audience as they have a better understanding of user behavior due to their advanced algorithms. 

In fact, The Cambridge Center for Alternative Finance found that more than 90% of global fintech companies are already relying heavily on artificial intelligence and machine learning. 

We can see now, industry trends for UI design and technologies, because statistics show a growing reliance on mobile digital devices in this modern digital era with consumers turning to them for everyday tasks.  

Here are some examples of the latest trends in mobile banking app technologies: 

– Chatbots: as mentioned before, chatbots are the new must-have in customer care, with 24/7 access to the full range of services, consumers now expect instant and personalized interactions with their banks. The welcome knock-on effect of this is increased customer retention combined with reduced support costs.  

– Biometric authentication: without knowing we all are using AI in our daily life, signing payments with fingerprints or face-based verification. This prevents phishing, identity theft, skimming and credit card hijacking. 

– Machine learning (ML): using this state-of-the-art technology, financial companies and banks are able to identify customer needs through user data analysis. One of the most useful applications is credit scoring by automatically identifying anomalies and monitoring transactional data, helping to instantly block attacks from hackers, spot suspicious activity and prioritize risk. 

-Voice-Based Payments: last but not least, this kind of payment makes transacting more straightforward for users. Platforms like Venmo, PayPal and Zelle already have customers using voice-activated and users agree that it is much easier than typing out phone numbers, card numbers, account numbers, or CVV numbers. Using voice to carry out the transaction makes payment quicker and hassle-free. 

Future trends in AI and fintech apps 

We can see that AI has become a necessity that is increasingly taking control of companies. Its application will spread across additional industries, particularly in the area of data access

Experts predict that as AI becomes more prevalent in finance, its application will spread across additional industries, particularly in the area of data access. For example, chatbots are the future of fintech customer service because they can handle a wide range of customer demands by AI technology rather than human call handlers. In fact, seems that successful banking-related chatbot interactions will grow 3,15% between 2019-2023; 826 million hours will be saved by banks through chatbot interactions in 2023 and 79% of successful chatbot interactions will be through mobile banking apps in 2023. 

Source: https://techemergent.com/ai-in-fintech/ 

Process control and optimization (PCO) is also an area that predicts big developments by using process mining and management tools, making improvements in the efficiency and productivity of their business processes. 

Finally, credit scoring and security will improve over the years reducing attrition and enhancing customer experience. 

Conclusion 

“An end objective is to be able to react at the speed of thought to changing situations, markets, and information: Making the best use of time because getting to understanding has never been a quick process in the history of business intelligence” Spencer Tuttle, SVP WW Sales at ThoughtSpot. 

As you just saw, AI can significantly enhance your FinTech app, because gives the financial industry a unique opportunity to reduce costs, improve customer experience and increase operational efficiency, among other things. All these solutions increase the productivity of fintech companies. Through artificial intelligence and machine learning, businesses could have personalized approaches toward their target audience as they have a better understanding of user behavior due to their advanced algorithms. 

Contact ThinkUp now to discuss your mobile wallet implementation needs and start your journey towards a more efficient, customer-centric future.

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